Employment

This sub-chapter shows an analysis of employment for different occupations in New York City.

Overview of Employment Distribution

In order to have an overview of employment distribution according to different occupations in New York City, we draw a Cleveland Dot Plot to show the 10-year-average of number of employed first.

# a general analysis with average numemp of all occupations by using cleveland dot plot

ggplot(AggregateData) +
  geom_point(aes(MeanNumEmp, reorder(Occupations,MeanNumEmp)),color = "royalblue3", size = 2, alpha = 0.75) + ylab('Occupation') + xlab('Number Employed') + 
  ggtitle('Number Employed by Occupation NYC') +
  scale_x_continuous(labels = ks)+
  mytheme

Observations on Number Employed by Occupation in NYC:

  1. Huge Differences in Number of Employed in Different Occupations.
  • There is a huge difference in number of employed among occupations. The sector with the biggest number of employed is office and administrative support, and the number of employed is 757015. However, for farming, fishing and forestry, which has the smallest number of employed, there’s only 4864 on average.
  1. Three clusters for employment.
  • The first group includes Office and administrative support, Management, Sales and related, which is a group with the biggest number of employed.
  • The second group includes the majority of occupations and have relatively similar number of employed among occupations.
  • The third group contains Farming, fishing, and forestry only, which is the group with the smallest number of employed.
  1. Gaps between the three clusters.
  • As can be seen from the plot, the gap between the first group and second group is very large, while the gap between the second and third group is relatively small.
  1. Top 3 and Last 3 Occupations in Employment.
  • Top 3
    • Office and administrative support
    • Management
    • Sales and related
  • Last 3
    • Farming, fishing, and forestry
    • Life, physical, and social science
    • Law enforcement workers

Analyze on Employment by Years

One of the things that affects employment is time. So first, we have an analysis on employment in different years.

General Trend of Employment by Years

ggplot(TrimmedAggregateYearlyData) +
geom_point(aes(YearlyNumEmp, reorder(Occupations,YearlyNumEmp), color = year),size = 2, alpha = 0.75) + ylab('Occupation') + xlab('Number Employed') + ggtitle('Number Employed by Occupation NYC 2010-2019') + scale_x_continuous(labels = ks) +
  mytheme

Observations on Number Employed by Occupation NYC through 2010-2019:

The most striking observations from this Cleveland dot plot are where there are clear trends over the decade, that coincide with intuition about the general trend of a sector. Some of the most striking trends are:

1.) Production occupations steadily decreasing over the course of the decade, the only sector where the number of employees for 2010 is lowest of the three years.

2.) Material moving occupations steadily increasing over the course of the decade.

3.) Computer and mathematical as well as architecture and engineering occupations quite quickly increasing over the course of the decade especially relative to their size.

4.) Health care support occupations growing at a stagnant rate from 2010 to 2015, but then growing rapidly from 2015 to 2019.

5.) Food preparation and serving related occupations growing steadily from 2010 to 2019.

In order to see the employment variances of the 25 occupations in detail, we draw boxplot for comparisons.

ggplot(YearlyNumEmps) +
  geom_boxplot(aes(x = YearlyNumEmp, y = reorder(x = Occupations,YearlyNumEmp, FUN = median)),
               color = "black", fill = "dark red", alpha = 0.7) + 
  ggtitle("Number working in sector per year") +
  xlab("Number Employed") + ylab("Occupations") + 
  mytheme1 +
  scale_x_continuous(labels = ks)

Observations from Boxplot of number working in sector per year:

Occupations that seem prone to dramatic fluctuation over time:

1.) Management occupations

2.) Office and administrative support occupations

3.) Health care support occupations

4.) Computer and Mathematical occupations

Occupations where the number of employees has fluctuated very little:

1.)Law enforcement

2.)Fire fighting

3.)Building and grounds keeping occupations

4.)Legal occupations

Something to be noted when reading this plot is that the number of people working in any particular occupation may change the viewers perception as to what constitutes more variation. To address this, a second plot has been created in which the box plots are normalized by dividing the number employed in each year by the mean across this sector. While the variation in total number employed won’t be apparent, the relative degrees of fluctuation will become more apparent.



ggplot(YearlyNumEmps) +
  geom_boxplot(aes(x = Normalized, y = reorder(x = Occupations,Normalized, FUN = median)),
               color = "black", fill = "dark red", alpha = 0.7) + 
  ggtitle("Normalized number working in sector per year") +
  xlab("Number Employed") + ylab("Occupations") +
  mytheme1

Observations from number working in sector per year:

In this plot, the total values of employees working in each field in each year are normalized by their averages. In doing this, changes in employment in a sector that on an absolute scale would be small, may be more pronounced as a function of variation relative to it’s own size.

In this plot, the average quantity across all years is normalized to one for illustrative purposes, which allows for new interpretations and findings:

1.) Fields such as management which had a large absolute spread from the years 2010-2019 aren’t necessarily the ones which seem to display the most variation with respect to themselves. While management had the largest box in the previous plot, it is in the middle of the pack in this one.

2.) Of the fields with the smallest numbers of workers: Farming, fishing and forestry occupations, Life, physical and social science occupations, Law enforcement occupations, firefighting occupations, and architecture and engineering occupations; The spreads across this decade were shown to be much more profound than in the previous plot, that is with the exception of law enforcement workers and firefighting workers. While the boxes grew somewhat in spread, the growth was not even nearly proportional to that of the other fields.

This indicates that the number of people working in law enforcement and firefighting is very stable, and this would make sense because a steady supply of people in these roles is necessary. With regards to the other occupations, it also makes sense that relatively unpopulated occupations would be more prone to greater variation as a percent of itself, since relatively few can leave or join, and a big impact can be had.

3.) As a percent of the number of employees working in each sector, the following were most prone to large shifts in number employed:

  • Farming, fishing and forestry
  • Material moving occupations
  • Computer and mathematical occupations
  • Health technologists and technicians
  • Healthcare support occupations

By contrast, these professions were the most stable:

  • Legal occupations
  • Sales occupations
  • Law enforcement workers
  • Fire fighters
  • Construction and extraction occupations

Percentage Difference by Years

# use a bar chart to order the percentage of range

ggplot(YearlyWithVariance,aes(x=fct_reorder(Occupations, range_pct),range_pct), y = range_pct) +
  geom_col(fill = "royalblue3",alpha = 0.75)+
  coord_flip()+
    theme(axis.text=element_text(),
      axis.title=element_text(face="bold"),
      plot.title = element_text(face = "bold"))+ xlab('Occupations') + ylab('Percentage Difference') + ggtitle('Percentage Difference of Employment over Years')+
  mytheme

Observations on Percentage Difference of Employment over Years:

  1. Top 5 and Last 5 in percentage difference in 2010 and 2019

Top 5

1.) Computer and mathematical occupations

This occupation type does not belong to the the group with most employed people among all the occupations. However, with in the past 10 years, this group has the biggest increase over the past 10 years.It is in a continuously increasing trend.

2.) Healthcare support occupations

This occupation has the second largest increase in number of employed, it is also in a continuously increasing trend.

3.) Architecture and engineering occupations

This occupation has also increased a lot in number of employed over the past 10 years, and in a continuously increasing trend.

4.) Farming, fishing, and forestry occupations

Surprisingly, this occupation ranks the fourth. This occupation has the least number of employed among all occupation types, and this might be one of the reasons that it can rank the fourth.

5.) Material moving occupations

This occupation type does not have a continuously increasing trend, the number of employed dropped a little from 2010 to 2015, then increased a lot from 2015 to 2019, which is different from other occupations that ranks top 5.

Last 5

1.) Installation, maintenance, and repair occupations

This group varied the least between 2010 and 2019. Although it was not the occupation with low number of employees, it had the smallest variation in number of employees. Also, this occupation increased in number of employed from 2010 to 2015, then dropped from 2015 to 2019.

2.) Sales and related occupations

Although this occupation has large number of employed, the number of employed has not increased a lot over the past 10 years. It is not in a monotonous trend as well, it increased a little from 2010 to 2015, then decreased a lot from 2015 to 2019.

3.) Legal occupations

This occupation is very stable in number of employed over the past 10 years. The number of employed increased from 2010 to 2015 and then decreased slightly from 2015 to 2019.

4.) Building and grounds cleaning and maintenance occupations

The number of employed for this group did not change a lot from 2010 to 2015, while dropped a little from 2015 to 2019.

5.) Personal care and service occupation

This occupation type is not very stable in number of employed compared with other occupations in the group of last 5. The number of employed people increased a lot from 2010 to 2015, and then decreased even more from 2015 to 2019, which makes the variance of number of employed between 2010 and 2019 in this occupation not very big.

Analyze on Employment by Counties

parcoords(YearBoroughData.wide,
          rownames = F,
          brushMode = "1D-axes",
          reorderable = T,
          queue = T,
          color = list(
            colorBy = "County",
            colorScale = "scaleOrdinal",
            colorScheme = "schemeCategory10"
          ),
          withD3 = TRUE)

Plot abbreviations key:

MBSC: Management, business, science, and arts
NRCM: Natural resources, construction, maintenance
PTMM: Production, transportation, material moving

Note: Each line represents one year. The numbers on the axis represent the proportion of the working population of that county working in each of the categories for that year. The numbers across each sector for each year sum to one.

Observations from sector groups employment across counties:

Observing this plot is that certain counties stand out in terms of how many of their people work in certain areas:

New York has great representation in Management, business, science, and arts with roughly 60% of its population routinely working in those fields across 2010-2019, which is at roughly 15% higher than for any other county.

Bronx County has great representation in service occupations, with roughly 35% of its population working routinely in this sector, which is at least 10% higher than for any other county.

Richmond County and Queens County seem to have good representation in natural resources, construction, and maintenance occupations.

New York, perhaps because of the high representation in the management, business, science, and arts cluster has clearly the lowest representation in service, natural resources, construction and maintenance, and production, transportation, management and material moving. New York also has the lowest representation in several years in Sales and Office occupations.

Of all the counties, Queens county, Kings county, and Richmond county seem to track each other most, and have the most similar levels of representation across the sector clusters.

Of all the counties, New York county and Bronx county seem to be the most different. Across almost each sector, low values for one almost certainly means that high values will appear for the other.

Analyze on Employment by Genders

ggplot(GenderData, aes(MeanNumEmpbyGender,fct_reorder2(Occupations,Gender=='Female',MeanNumEmpbyGender, .desc=FALSE), color = Gender)) + 
  geom_point(size = 2, alpha = 0.7) +
  ylab('Occupations') + xlab('NumEmps') + ggtitle('Number Employed by Occupation and Gender in NYC') +
scale_x_continuous(labels = ks) + scale_color_manual(values=c('seagreen3','mediumorchid')) +
  mytheme

Observations on Number Employed by Occupation NYC for different genders:

A broad takeaway from studying this plot is that generally men tend to dominate the majority of professions, with 17 occupations being majority male, and only 8 occupations being majority female.

The occupations in which females account for the vast majority of employees are Office and administrative support, education, training, and library, healthcare support, and personal care and service.

The occupations in which males account for the vast majority of employees are construction and extraction, transportation, and installation, maintenance and repair.

ggplot(GenderData.tidy,aes(x = fct_reorder2(Occupations,Gender == "Female",prop,.desc=FALSE), y=prop,fill=Gender,group=Gender)) + 
  geom_bar(position='fill', stat='identity')+
  coord_flip()+
  ylab('Proportion') + xlab('Occupations') + ggtitle('Gender Composition by Occupation')+
  scale_fill_manual(values=c('mediumorchid','seagreen3')) +
  mytheme

Observations from gender composition by occupation

From this plot, we can list the top ten occupations with high percentages of male and the top 10 occupations with high percentages of female.

Top 10 occupations for males

  1. Construction and extraction occupations

  2. Installation, maintenance and repair occupations

  3. Transportation occupations

  4. Material moving occupations

  5. Architecture and engineering occupations

  6. Fire fighting and prevention, and other protective service workers including supervisors

  7. Computer and mathematical occupations

  8. Law enforcement workers including supervisors

  9. Farming, fishing and forestry occupations

  10. Food preparation and serving related occupations

Top 10 occupations for females

  1. Healthcare support occupations

  2. Personal care and service occupations

  3. Health, design and treating practitioners and other technical occupations

  4. Education, training and library occupations

  5. Health technologists and technicians

  6. Community and social service occupations

  7. Office and administrative support occupations

  8. Life, physical, and social service occupations

  9. Business and financial operations occupations

  10. Legal occupations


    Besides, we want to see if there’s a relation between gender and the variation of number employed by years. Therefore, we draw a bar chart of variance of different occupations in year 2010 and year 2019, and use different color of bars to represent whether this occupation is male-dominated or demale-dominated.

GenderData.tidy$`Gender Distribution` <- ifelse(
  ( 
        (GenderData.tidy$Gender == "Female" & GenderData.tidy$prop < 0.5) |
        (GenderData.tidy$Gender == "Male" & GenderData.tidy$prop > 0.5)
    ),
  "Male-dominated",
  "Female-dominated"
)

#write.csv(GenderData.tidy,"/Users/tracy/Documents/GitHub/STAT5702_NYC_Employment_Analysis/DataPreprocessing/Data/NumEmp/TidyGenderData.csv", row.names = FALSE)

ggplot(GenderData.tidy,aes(x=fct_reorder(Occupations, difference_pct,.desc = T), y = difference_pct)) +
  geom_col(fill = ifelse(GenderData.tidy$`Gender Distribution` == "Male-dominated","orange","green"))+
    mytheme+
  ylab('variance_pct') + xlab('Occupations') + ggtitle('Variance of Number Employer by Occupations')

From this plot, we can discover that, for the top 10 occupations with greatest variances, there are 5 male-dominated occupations and 5 female-dominated occupations. However, for other occupations, the majority of them are male dominated.

# the range is the range by year
YearlyWithVariance <- YearlyWithVariance %>% select(1,3)
GenderData.tidy <- GenderData.tidy %>% select(1,7) %>% unique()
TotalData <- merge(YearlyWithVariance,GenderData.tidy,by="Occupations")

ggplot(TotalData,aes(x=fct_reorder(Occupations, range_pct, .desc = T), y = range_pct)) +
  geom_col(fill = ifelse(TotalData$`Gender Distribution` == "Male-dominated","orange","green"))+
    theme(axis.text=element_text(size=30,angle = 90, vjust = 0.5, hjust=1),
      axis.title=element_text(size=40,face="bold"),
      plot.title = element_text(size = 50, face = "bold"))+
  ylab('variance') + xlab('Occupations') + ggtitle('Variance of Number Employer by Occupations')



PropsOfPropsRace$Occupation <- as.factor(PropsOfPropsRace$Occupation)
PropsOfPropsRace$Occupation <- fct_reorder(PropsOfPropsRace$Occupation, PropsOfPropsRace$White, .desc = FALSE)
PropsOfPropsRaceTidy <- pivot_longer(PropsOfPropsRace, cols = c('White','Asian','Hispanic','Black'), names_to = 'race', values_to = 'Proportion') 
PropsOfPropsRaceTidy$Occupation <- as.factor(PropsOfPropsRaceTidy$Occupation)
ggplot(PropsOfPropsRaceTidy, aes(fill = race, y = Occupation, x = Proportion)) + geom_bar(position = 'stack',stat = 'identity') + ggtitle('Race Proportions by Sector Given Equal Populations') + 
  mytheme2

Observations from Relative Likelihood of Sector Employment by Race:

Note: This plot was formed by comparing the proportions of each race group working in each sector. If for a particular sector each group accounts for 0.25 in this plot, then each race group has the same percentage of its people working in that sector. This has the effect of displaying what the race demographics of a sector would look like assuming all races had the same population.

1.) It seems that a typical white person is much more likely than persons of other races to work legal profession compared to other races. They are also quite likely compared to other races to engage in Management occupations, physical, life and social science occupations, and art, design, entertainment, sports and media occupations. 

2.) It seems that a typical white person is much less likely than persons of other races to work in health care support occupations, transportation occupations, production occupations, personal care and service occupations, material moving operations, and building and grounds cleaning and maintenance occupations 

3.) It seems that a typical Hispanic person is much more likely than person of other races to work in Farming, Fishing and forestry occupations. They are also quite likely compared to other races to engage in building and grounds cleaning and maintenance occupations, construction and extraction operations, and material moving operations. 

4.) It seems that a typical Hispanic person is much less likely than persons of other races to work in health diagnosing and treating practitioners and other technical occupations, business and financial operations, computer and mathematical occupations, legal occupations, art, design, entertainment, sports, and media occupations, architecture and engineering occupations, and life, physical, and social science occupations. 

5.) It seems that a typical Black person is much more likely than persons of other races to work in fire fighting and prevention and other protective services including supervisors, law enforcement workers including supervisors, healthcare and support occupations, and community and social service occupations. 

6.) It seems that a typical Black person is much less likely than persons of other races to work in architecture and engineering occupations, art, design, entertainment, sports and media occupations, farming, fishing, and forestry occupations, food preparation and serving occupations, life, physical and social science occupations, and legal occupations. 

7.) It seems that a typical Asian person is much more likely than persons of other races to work in computer and mathematical operations, architecture and engineering operations, business and financial operations, health diagnosing and treating practitioners and other technical occupations. 

8.) It seems that a typical Asian person is much less likely than persons of other races to work in Law enforcement occupations, fire fighting and prevention occupations, community and social service occupations, and building and grounds cleaning and maintenance occupations.\